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Data Warehouse Integrations & Reverse ETL Explained

Posted September 15, 2025 in HubSpot, Technology by

HubSpot connects to external data warehouses through Data Studio, using native connectors for Snowflake, Google BigQuery, Databricks, and Amazon Redshift (currently in beta), plus a CSV-only sync option for Amazon S3. These connectors bring warehouse data into HubSpot datasets and, for Snowflake, BigQuery, and S3 specifically, can sync it back out again through reverse ETL. All of them require a Data Hub Enterprise subscription.

That’s the short answer. Here’s what it actually means in practice, and what each connector requires.

Today’s businesses run on data, but it’s often stuck in too many places. Marketing lives in HubSpot, sales in CRM records, finance in an ERP, and historical data sits in a data warehouse like Snowflake or Databricks.

The challenge: warehouses are powerful storage engines, but they’re not built for easy, everyday access. That’s where HubSpot’s Data Hub steps in, with warehouse connectors and reverse ETL inside Data Studio.

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What Is a Data Warehouse?

A data warehouse is a centralized system for storing, managing, and analyzing large volumes of structured data — a historical archive of sales transactions, customer records, product usage, and more.

Examples include:

  • Snowflake
  • Google BigQuery
  • Databricks
  • Amazon Redshift

Warehouses are excellent for storage and analysis, but they’re generally not friendly to non-technical teams. Getting data out usually means SQL, an analyst, or a ticket to IT.

What Is Reverse ETL?

To understand reverse ETL, start with ETL itself:

  • ETL (Extract, Transform, Load): pulling data out of your operational systems, cleaning it, and loading it into a warehouse for long-term storage and analysis.
  • Reverse ETL: the opposite motion — moving data out of the warehouse and back into the tools your team actually works in, like HubSpot.

Reverse ETL is what makes warehouse data actionable instead of just archived. Rather than living in a report only an analyst opens, it flows into CRM records, segments, and workflows.

The Native Warehouse Connectors in HubSpot Data Studio

This is where it’s worth being precise, because HubSpot’s product menu groups a few different kinds of connections together, and they don’t all work the same way.

Inside Data Studio, under Add external data source, the current native warehouse options are:

  • Snowflake Data Ingestion
  • Google BigQuery Data Ingestion
  • Databricks
  • Amazon Redshift (beta)

Each of these connects a warehouse account, lets you select the data sources and columns to bring in (up to 200 columns per source), and syncs the result into a HubSpot dataset — which you can then blend with CRM data, use in reports, or activate in workflows and segments.

Amazon S3 also appears in the same menu, but it isn’t a true warehouse connector. It only syncs CSV files from a folder you specify, and by default, it syncs whichever CSV is most recent — not a live table or query. It’s a legitimate and simple option if your data already lands as scheduled CSV exports, but it’s a fundamentally different tool than Snowflake, BigQuery, or Databricks, even though HubSpot lists it alongside them.

Amazon Redshift is the newest addition, and it’s currently in beta. A Super Admin has to opt the account into the Data Studio beta before it’s available. Once connected, Redshift is also the fastest-refreshing option — data syncs automatically every five minutes, compared to the scheduled syncs on other connectors. Setup involves more than a few clicks in HubSpot: you’ll create an AWS role, supply its ARN, and update permissions in both AWS and Redshift before the connection works.

On the reverse ETL direction specifically: HubSpot’s officially supported bidirectional integrations — meaning data flows both into and out of HubSpot — are Snowflake, BigQuery, and Amazon S3. Databricks and Redshift currently sync data into HubSpot through Data Studio; treat any two-way flow with those two as something to verify against your specific setup rather than assume.

What You Need Before You Connect One

A few requirements apply across the board:

  • Data Hub Enterprise is required for warehouse connectors in Data Studio. This isn’t available on lower Data Hub tiers.
  • 200 columns per external data source is the sync cap for warehouse connectors (100 for CSV sources).
  • Building datasets from warehouse data, like creating calculated fields or joining sources, draws on HubSpot Credits. Simply syncing one warehouse source on its own does not.

None of this is complicated, but it’s worth confirming before you scope a project around a specific connector — especially if beta access or AWS-side configuration is part of the plan.

Why This Matters for Your Business

  • Marketing can build segments using complete customer intelligence instead of a monthly export.
  • Sales gets richer context — product usage, billing status, churn risk — directly inside deal and contact records.
  • Service can see full customer history without switching tools.
  • RevOps finally connects warehouse-scale data with the day-to-day systems teams actually use.

Instead of choosing between “big data” locked in a warehouse and “usable data” in a CRM, a properly configured connector gives you both.

The Bottom Line

Data warehouses are powerful, but they’re only half the story. With the right connector, and a clear understanding of what each one actually does, Data Studio turns warehouse-scale data into something your whole team can act on, not just something your analysts can query.

The right choice depends on where your data already lives, how fresh it needs to be, and whether you actually need a full warehouse connector at all. For a closer look at making that call, get in touch with our team.

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